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Data Analyst with less than a year in Machine Learning & Data Visualization.
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Data Science Engineer skilled in Python (Pandas, NumPy, scikit-learn) and SQL/PostgreSQL, with hands-on experience delivering end-to-end ML and analytics projects. Experienced in data ingestion, cleaning, feature engineering, model development and productionization, plus dashboarding for stakeholder insights. Proven ability to turn complex data into actionable solutions that improve decision-making and drive measurable business impact.
University of Mysore - Sarada Vilas College
B.Sc. (Hons) · Data Science & Artificial Intelligence
January 1, 2021 – January 1, 2025
Gopalaswamy Independent Composite PU College
Pre-University (PCMB) · PCMB
N/A – Present
Unified Mentor
Data Analyst Intern
June 1, 2024 – July 1, 2024
India
Twitter Sentiment Analysis
June 19, 2026 – Present
Problem: Classified public sentiment on Twitter to surface customer perception trends for topical events. Methods: Cleaned and tokenized raw tweets using NLTK, applied lemmatization and stopword removal, and engineered features with TF-IDF and n-grams; experimented with Logistic Regression, SVM and Naive Bayes classifiers. Evaluation & Improvement: Conducted error analysis and targeted preprocessing that improved classification accuracy by ~10%; used confusion matrices to tune class-specific thresholds. Delivery: Produced reproducible preprocessing pipelines and model scripts suitable for batch scoring; documented assumptions and monitoring checks for data drift.
Commercial Vehicle Fuel Prediction
February 1, 2025 – July 1, 2025
Problem: Estimated fuel consumption for commercial vehicles to inform route planning and reduce operating costs. Methods: Aggregated telematics and vehicle specification data, performed EDA to detect outliers and seasonality, engineered domain-specific features (load, speed profiles, ambient conditions), and trained Random Forest Regression. Optimization: Applied feature selection and hyperparameter tuning (grid search with cross-validation) to reduce prediction error by 12% over baseline models. Deployment & Impact: Delivered model artifacts and scoring scripts with clear instructions for integration; recommended monitoring metrics to measure fuel prediction accuracy and support operational decisions.
Heart Disease Prediction System
September 1, 2024 – February 1, 2025
Problem: Predicted risk of heart disease from patient clinical data to support early intervention decisions. Methods: Performed exploratory data analysis to identify signal and missingness patterns, engineered clinical and derived features (e.g., BMI categories, cholesterol ratios), and compared Logistic Regression, Random Forest and XGBoost models. Evaluation: Selected a calibrated Logistic Regression model after cross-validation and ROC analysis; documented feature importance and model limitations. Deployment & Deliverables: Packaged the model behind a lightweight Flask API for real-time predictions and a simple web UI for clinician input; included data validation checks and logging for traceability. Impact: Improved screening efficiency by enabling rapid, reproducible risk scores and providing interpretable model features to support clinical discussions.
Cloud Computing NPTEL 12 weeks 2025 Score 62
NPTEL
October 1, 2025 – Present
Big Data Computing NPTEL 8 weeks 2025 Score 79
NPTEL
October 1, 2025 – Present
Data Analyst Certification Unified Mentor, Exploratory Data Analysis EDA Certification
Unified Mentor
August 1, 2024 – Present
Cultural Fit Analysis
The candidate's academic projects cover diverse applications of data analysis and machine learning, including sentiment analysis, medical prediction, and commercial vehicle optimization, showcasing a broad interest in applying data science to different domains. The internship experience as a Data Analyst Intern directly aligns with the target role, providing practical exposure to industry standard tools and processes. The certifications in Big Data Computing and Cloud Computing, although future-dated, indicate a proactive approach to continuous learning and staying updated with relevant technologies, which is a positive cultural fit for a dynamic tech environment. The candidate's involvement in sports also suggests a team-oriented and disciplined personality.
Soft Skills & Operational Fit
The candidate demonstrates good problem-solving and analytical thinking skills through their project work, where they identified problems, applied various methods, and evaluated solutions. Collaboration and teamwork are mentioned in the internship description, indicating an ability to work in cross-functional teams and participate in Agile methodologies. Communication is also listed as a soft skill, with an emphasis on tailoring findings to stakeholders, which is crucial for a Data Analyst role. The project descriptions are well-structured, detailing problem, methods, evaluation, and impact, suggesting good communication of technical work.